English
导航菜单
By Jun Huan, Ph.D.
Department of Department of Electrical
Engineering and Computer Science
University of Kansas
Graphs are
widely used modeling tools that represent objects and their relation (links). Graph
modeled data are found in diverse application areas including bioinformatics,
cheminformatics, social networks, wireless sensor networks among many others.
In this talk we will survey the field of graph mining and graph learning, present
our recent work on various directions, with a focus on scalable algorithmic
approaches for big graph data. Related topics, such as multi-task learning and
multi-view learning will be touched. At the end of the talk we will briefly
introduce the applications of graph mining and learning techniques in selected
application areas.
Short Bio of Dr. Huan： Dr. Jun (Luke) Huan is an Associate Professor in the Department of
Electrical Engineering and Computer Science at the University of Kansas. He
directs the Bioinformatics and Computational Life Sciences Laboratory at KU
Information and Telecommunication Technology Center (ITTC) and the
Cheminformatics core at KU Specialized Chemistry Center. Dr. Huan holds
courtesy appointments at the KU Bioinformatics Center, the KU Bioengineering
Program, and a visiting professorship from GlaxoSmithKline plc..
Dr. Huan received his Ph.D. degree in Computer Science from the
University of North Carolina at Chapel Hill. Before joining KU in 2006, he
worked at Argonne National Laboratory and GlaxoSmithKline plc.. Dr. Huan was a
recipient of the National Science Foundation Faculty Early Career Development
Award in 2009. He has published more than 80 peer-reviewed papers in leading
conferences and journals including Nature Biotechnology. His group won the Best
Student Paper Award at IEEE International Conference on Data Mining in 2011 and
the Best Paper Award (runner-up) at ACM International Conference on Information
and Knowledge Management in 2009. Dr. Huan served on the program committees of
prestigious international conferences including ACM SIGKDD, ACM CIKM, ICML,
IEEE ICDE, IEEE ICDM, and IEEE BigData.
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